MLS-C01 Modeling Practice Question
Which TWO of the following are valid techniques for handling missing values in a dataset for machine learning?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Remove rows with missing values
The valid techniques for handling missing values are removing rows with missing values and replacing missing values with the mean of the feature. Options B and E are correct. Option A (maximum value) introduces bias, option C (random noise) distorts distribution, and option D (string conversion) is inappropriate for numerical data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Replace missing values with the maximum value of the feature
Why it's wrong here
Replacing missing values with the maximum value introduces bias and is not a recommended technique.
- ✓
Remove rows with missing values
Why this is correct
Removing rows with missing values is a valid technique, especially when the missing data is few and random.
- ✗
Replace missing values with random noise
Why it's wrong here
Adding random noise is not a standard method for handling missing values.
- ✗
Convert missing values to the string 'missing'
Why it's wrong here
Converting missing values to the string 'missing' is not appropriate for numerical features.
- ✓
Replace missing values with the mean of the feature
Why this is correct
Mean imputation is a common and valid technique for handling missing numerical values.
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